Overexpression of Antimicrobial, Anticancer, and Transmembrane Peptides in <i>Escherichia coli</i> through a Calmodulin-Peptide Fusion System
Bibliographic record
Abstract
In recent years, the increasing number of antibiotic-resistant bacteria has become a serious health concern. Antimicrobial peptides (AMPs) are an important component of the innate immune system of most organisms. A better understanding of their structures and mechanisms of action would lead to the design of more potent and safer AMPs as alternatives for current antibiotics. For detailed investigations, effective recombinant production which allows the facile modification of the amino acid sequence, the introduction of unnatural amino acids, and labeling with stable isotopes for nuclear magnetic resonance (NMR) studies is desired. Several expression strategies have been introduced in previous reports; however, their effectiveness has been limited to a select few AMPs. Here, we have studied calmodulin (CaM) as a more universal carrier protein to express many types of AMPs in E. coli. We have discovered that the unique architecture of CaM, consisting of two independent target binding domains with malleable methionine-rich interaction surfaces, can accommodate numerous amino acid sequences containing basic and hydrophobic residues. This effectively masks the toxic antimicrobial activities of many amphipathic AMPs and protects them from degradation during expression and purification. Here, we demonstrate the expression of various AMPs using a CaM-fusion expression system, including melittin, fowlicidin-1, tritrpticin, indolicidin, puroindoline A peptide, magainin II F5W, lactoferrampin B, MIP3α51-70, and human β-defensin 3 (HBD-3), the latter requiring three disulfide bonds for proper folding. In addition, our approach was extended to the transmembrane domain of the cell adhesion protein l-selectin. We propose the use of the CaM-fusion system as a universal approach to express many cationic amphipathic peptides that are normally toxic and would kill the bacterial host cells.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".